Homework Project – Protein Structure Prediction Contact me if you have any questions/problems with the assignment: [email protected] For this homework, each of you will be assigned a particular human protein to model. I have picked out proteins of interest in cancer biology. I have colleagues at Fox Chase Cancer Center who work on some of these, and others of them are sequenced in gene panels given to our patients. We are trying to develop predictors of the phenotypes of missense mutations in some of these genes, and having better models would be really helpful. So this homework is like a group project to improve the interpretation of gene sequencing data for real patients. I hope it will be fun and interesting. If the results are publishable, we may consider submitting one or more of these to PLOS ONE for publication. The goal of the homework is to use structure prediction servers to obtain models of the assigned protein and to interpret as much biology on these proteins as possible in light of the models. As I showed during the lectures and demonstration, each available server has strengths and weaknesses and greater or lesser flexibility in choosing templates and visualizing the results. In this assignment, I want you to use several different servers for structure prediction and to gather biological information from as many sources as possible. Perform the following tasks and provide a write-up in the form of a short paper that might appear in a journal with an Abstract, Introduction, Methods, Results, and Discussion. As a guide, the lengths of each section should be at least: Abstract – 150 words, Introduction – 500 words, Methods – 250 words, Results – 1000 words, Discussion – 500 words. They can be longer or shorter as needed, but I want you to think about the modeling results, not just run some webservers and make some images. You should include figures to show interesting features of the models. Such features might include active sites, the positions of known disease mutations or even variants of unknown significance (VUS), the structures of homo- or heterooligomeric complexes or complexes with ligands, inhibitors, or nucleic acids. Here are the steps you should go through and the things you should look at in the structure predictions. 1. Get the protein sequence from Uniprot. Learn something about the function of the protein, what it interacts with, is it an enzyme, are there disease-related mutations, have people made mutations experimentally and do these have a phenotype, are there different splice forms (isoforms), etc. There are links to protein-protein interaction databases that you can use to find additional protein interactors. You will need all this information for the introduction, results, and discussion sections. 2. Run at least three different disorder predictors on the sequence. There is a list here: http://www.disprot.org/predictors.php. IUpred, PONDR-FIT, Spritz, and DISOPRED are all good servers, but some of the others might be interesting. Are the predictions consistent? Are there long regions of disorder (>30 amino acids) and where are they? You can download the images of the disorder prediction that the servers provide and line them up by scaling the size of each image horizontally. Or (even better) you can download the raw data and put them into a graphing program (R, Matlab, even Excel), and put them into one graph. Make sure you understand what is the cutoff for something to be predicted as “disordered” (most of them are 0.5 on a 0 to 1.0 scale, but DISOPRED has a 0.05 cutoff. Here’s an example of human STK6. 3. Run the Pfam server to find the Pfam domains in your protein sequence: http://pfam.xfam.org/search. Use the Pfam site to find out about each Pfam domain – what it does, what proteins the domain is found in. Present the results as a bar plot of the domains (like you see in most papers on proteins). Powerpoint would work. You should mark the residue numbers where each domain starts and ends (which are often missing in papers). You can add as many annotations as you can find on where the domains are, where the disorder is (from step 1), where any post-translational modification sites are, where disease-related mutations are, and where other molecules are known to interact. Something like this: 4. Look up the Pfams on ProtCID: http://dunbrack2.fccc.edu/protcid (under the browse button, look for the first letter of the Pfam and then under that link, look for the Pfam). It’s possible some Pfams from the Pfam website will not be on ProtCID, either because there really are no structures in that Pfam, or because ProtCID is based on Pfam version 27, while the website is on version 29. We are updating the site but it’s not done yet. When you look up a Pfam, you get a page of hits that describes what is in each structure that contains the Pfams – the Pfam architecture of the chain contains that has the Pfam (under “Chain Arch”), other chains in the structure including peptides (under “Entry Arch”), the biological assemblies of all the entries that contain the Pfam (e.g. PDBBA = A2; PISABA = A4 for homodimer and homotetramers; A2B2 is a heterotetramer of two different kinds of proteins). Then look at the ligands in the last column. If you click on each one, you will get a description of the ligand. Or you can click on the link at the top that says “Click for description of ligands.” Here is a snapshot: The E-value column will tell you whether the Pfam is a strong hit for that PDB sequence. Generally values smaller than 1.0e-5 are quite safe. You can also tell if the PDB sequence has the whole Pfam by looking at the HmmBeg and HmmEnd columns. The length of the HMM is given at the top of the page. From this analysis, you should write up some comments about what kinds of biological assemblies the Pfam makes, what type(s) of ligands it binds, and observe whether more than one of the Pfams in your query sequence is present in a single chain in the PDB, or potentially in heterodimers in the PDB. It occurs occasionally that multi-domain proteins can be modeled off of protein complexes in the PDB, where the domains of the target protein are actually in different chains in the template. What you learn from this page might be relevant when picking templates on the websites that allow you to choose templates, since it will be easy to superimpose the template onto the model so that you can add the ligands/peptides/nucleic acids/other proteins from the template to your models. 5. Submit your sequence to as many servers as you can, but at least 5. Give them your email address so you can access the results later. If they provide a web link to the results, include that in your report so I can look at it as well. Some of these will take a few days, so submit early so you have the results. http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index http://toolkit.tuebingen.mpg.de/hhpred (remember: to use Modeller, the password is MODELIRANJE). http://zhanglab.ccmb.med.umich.edu/I-TASSER/ http://robetta.bakerlab.org/submit.jsp http://www.reading.ac.uk/bioinf/IntFOLD/ http://swissmodel.expasy.org/ http://raptorx.uchicago.edu/StructurePrediction/predict/ http://sparks-lab.org/yueyang/server/SPARKS-X/ http://manaslu.aecom.yu.edu/M4T/ When you can choose your templates on a webserver, think about what to select. Templates that cover more of the sequence are good, as are ones that have the right ligands in them (e.g. DNA). Picking multiple templates (where the server gives you that option) that are different from each other is helpful. You can check their source organisms and sequences on the PDB’s website, if the prediction server does not provide that information. 6. Download the models and accompanying information provided by the servers, which may include alignments, secondary structure predictions, sequence identity, significance scores for the hits (e.g., E-values), and model quality assessment scores, if they are provided. MQA scores are predicted accuracies of the models – what would be expected at that sequence identity or sequence alignment score. Summarize the models you downloaded in a table – templates (PDB codes and protein names and species, resolution of the structures, etc.), sequence identity, significance scores, what regions of the target are covered, etc. 7. Align the model structures either within VMD or Pymol, or even better use a multiple structure alignment server like one of these: POSA http://posa.godziklab.org/ Mammoth-Mult http://ub.cbm.uam.es/software/online/mamothmult.php Mustang http://pxgrid.med.monash.edu.au:8080/mustangserver/ Matt http://matt.cs.tufts.edu/ MultiProt http://bioinfo3d.cs.tau.ac.il/MultiProt/ After aligning the programs, visualize them in VMD or Chimera or Pymol. Where are there differences in the models, especially in the backbone coordinates? Are the models complete (with no internal gaps in the structure)? If the models are different in loops or the placement of secondary structure, is this due to the template that was used? (you can download the templates from the PDB and superimpose them on the models to see where the placement of loops or secondary structures might have come from). 8. If possible, try to model the protein in complex with some other molecule. Looking at the available templates, do they have ions, small molecules (whether inhibitors or ligands or substrates), peptides, nucleic acids, or other proteins? If the target interacts with other proteins (from the Uniprot page), is there a structure in the PDB with Pfams from your target and one of the interactors (this can be a lot of work to determine, since you have to identify the Pfam of the interactors and then determine if there is a structure or template for that protein, and for the complex (which you can determine with ProtCID).